code: def cost_forecast_meta(clf, X, base_estimator_names):
      """Forecast model cost by pretrained cost estimator.
  
      Parameters
      ----------
      clf : object, sklearn regressor
          Random forest regressor trained to forecast model cost
  
      X : numpy array of shape (n_samples, n_features)
          The input samples.
  
      base_estimator_names : list of str
          The list of outlier detection model names in the string format
  
      Returns
      -------
      time_cost_pred : numpy array of outlier detection model cost in seconds.
      """
      # convert base estimators to the digestible form
      clf_idx = np.asarray(
          list(map(clf_idx_mapping.get, base_estimator_names))
      )
  
      X_detector_code = indices_to_one_hot(clf_idx - 1, 11)
      X_shape_code_s = np.array([X.shape[0], X.shape[1]]).reshape(1, 2)
      X_shape_code = np.repeat(
          X_shape_code_s, len(base_estimator_names), axis=0
      )
      X_code = np.concatenate((X_shape_code, X_detector_code), axis=1)
      time_cost_pred = clf.predict(X_code)
  
      return time_cost_pred
  
file_path: SUOD/suod/models/parallel_processes.py
imports: from sklearn.base import clone
  import numpy
  from scipy.stats import rankdata
  from joblib import effective_n_jobs
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from pyod.utils.utility import score_to_label
  from .jl_projection import jl_fit_transform, jl_transform
  from ..utils.utility import raw_score_to_proba
label_desc: Use image node_id 4 for calling a global function with example usage: cost_forecast_meta(clf, X, base_estimator_names) and returns: time_cost_pred